103 citations · 104 across the 16 of their papers we have counts for
9 papers · 1 filter
Learning Hierarchical Skill Policies with Offline Quality-Diversity Reinforcement Learning
Tanachai Anakewat, Takayuki Osa, Tatsuya Harada
Recent studies investigate how to leverage pre-collected datasets to improve the policy performance and sample efficiency of RL. One promising approach to achieve this goal is to e…
Learning Physical Interaction: A Survey of Tactile- and Force-aware Robot Learning
Shilin Shan, Chuhao Zhou, Ruize Wang +30
Physically grounded robot intelligence requires robots to perceive, reason about, and regulate their interactions with the physical world. This capability is particularly critical…
Learning the Supports for Categorical Critic in Reinforcement Learning
Jen-Yen Chang, Takayuki Osa, Tatsuya Harada
Value functions are an essential component in actor-critic based deep reinforcement learning (RL). Conventionally, these functions are trained as a regression task by minimising th…
Self-Supervised Theorem Discovery in a Formal Axiomatic System
Kazuki Ota, Takayuki Osa, Tatsuya Harada
Recent artificial intelligence (AI) systems have shown remarkable progress in mathematical reasoning. Many existing approaches, including large language models (LLMs), draw on huma…
World Model for Robot Learning: A Comprehensive Survey
Bohan Hou, Gen Li, Jindou Jia +15
World models, which are predictive representations of how environments evolve under actions, have become a central component of robot learning. They support policy learning, planni…
R2-Dreamer: Redundancy-Reduced World Models without Decoders or Augmentation
Naoki Morihira, Amal Nahar, Kartik Bharadwaj +3
A central challenge in image-based Model-Based Reinforcement Learning (MBRL) is to learn representations that distill essential information from irrelevant visual details. While pr…
Rethinking Policy Diversity in Ensemble Policy Gradient in Large-Scale Reinforcement Learning
Naoki Shitanda, Motoki Omura, Tatsuya Harada +1
Scaling reinforcement learning to tens of thousands of parallel environments requires overcoming the limited exploration capacity of a single policy. Ensemble-based policy gradient…
Cross-Embodiment Offline Reinforcement Learning for Heterogeneous Robot Datasets
Haruki Abe, Takayuki Osa, Yusuke Mukuta +1
Scalable robot policy pre-training has been hindered by the high cost of collecting high-quality demonstrations for each platform. In this study, we address this issue by uniting o…
Revisiting Regularized Policy Optimization for Stable and Efficient Reinforcement Learning in Two-Player Games
Kazuki Ota, Takayuki Osa, Motoki Omura +1
Two-player games such as board games have long been used as traditional benchmarks for reinforcement learning. This work revisits a policy optimization method with reverse Kullback…